18 citations · 35 across the 10 of their papers we have counts for
5 papers · 1 filter
GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction
Kareem Metwaly, Aerin Kim, Elliot Branson +1
Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and…
CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles
Kareem Metwaly, Aerin Kim, Elliot Branson +1
Self-driving vehicles are the future of transportation. With current advancements in this field, the world is getting closer to safe roads with almost zero probability of having ac…
Natural Adversarial Objects
Felix Lau, Nishant Subramani, Sasha Harrison +3
Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce…
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models
Zeyad Emam, Andrew Kondrich, Sasha Harrison +4
High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL). However, since the e…
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
Matthew Groh, Caleb Harris, Luis Soenksen +5
How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision…